Populations of identified neurons express highly variable ionic current (or conductance) levels. However, subsets of these conductances are sometimes found to co-vary. This suggests the existence of a mechanism for the homeostatic control of neuronal activity whereby ionic currents controlling specific features of activity compensate for each other to preserve or stabilize that neuronal activity trait. This has been confirmed mostly by theoretical studies. However, the actual roles that conductance correlations play in neuronal networks are poorly understood. The goal of this proposal is to test the hypothesis that Neurons use the correlated expression of intrinsic and synaptic ion channels to maintain their activity bounded within more or less narrow limits. This ensures stability of activiy as ionic conductance changes occur (such as those that take place during growth, or result from neuromodulatory effects, and activity-dependent conductance modifications). We further propose that such Ionic current correlations contribute to tuning individual cells to optimally respond to natural inputs (synaptic for example) between neurons within a network that may themselves vary from animal to animal. We suggest that both of these phenomena are related by the property of co-varying conductances. We will test these hypotheses using, primarily, voltage clamp (to measure -the variable- current levels in identified target neurons that form the core of the rhythm- generating pyloric network of crabs), and dynamic clamp techniques to manipulate these currents (in order to examine the stability of neuronal and network activity), as well as computational modeling of neurons and networks. We will use quantitative measures to evaluate stability of activity and to compare experimental with theoretical data. Oscillatory systems are ideal to study these questions because their recurrent dynamics offers well defined activity attributes to quantify the system's behavior. Moreover, oscillatory systems underlie vital functions in most animals, such as respiration, heartbeat, locomotion, digestion, etc. Thus, understanding the mechanisms that generate rhythmic behaviors and that regulate their stability is essential to develop strategies and therapies to maximize our ability to prevent dysfunction or recovery from neurological disease and injury. The model system we study is ideally suited for this work because its component neurons are very few (reduced to 3 in this case), because all the ionic currents are known and can be measured in individual cells, and because there are distinct activity features that the system naturally maintains constant across individuals, providing a clear and convenient assay for our hypotheses. Success to uncover how robustness of network- wide properties is achieved is expected to provide a new framework to understand homeostasis in the nervous system and to guide future research in the field.